Modeling fMRI Data: Challenges and Opportunities

Alberto Maydeu-Olivares () and Gregory Brown ()

Psychometrika, 2013, vol. 78, issue 2, 240-242

Abstract: We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data—the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling. Copyright The Psychometric Society 2013

Keywords: network analysis; cluster analysis; three-way data; principal components analysis; independent components analysis; structural equation models; generalized linear models (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11336-013-9332-6

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